Using some performance parameters to predict exhaust gas emissions of a turboprop engine: adaptive neuro-fuzzy method

ŞÖHRET Y., YAZAR I., KARAKOÇ T. H.

INTERNATIONAL JOURNAL OF SUSTAINABLE AVIATION, vol.2, no.1, pp.1-14, 2016 (ESCI) identifier

  • Publication Type: Article / Article
  • Volume: 2 Issue: 1
  • Publication Date: 2016
  • Journal Name: INTERNATIONAL JOURNAL OF SUSTAINABLE AVIATION
  • Journal Indexes: Emerging Sources Citation Index (ESCI)
  • Page Numbers: pp.1-14
  • Keywords: aircraft emissions, adaptive network-based fuzzy inference system, ANFIS, combustion, neuro-fuzzy, prediction, turboprop, POLLUTANT EMISSIONS, AIRCRAFT EMISSIONS, INFERENCE SYSTEM, TURBINE ENGINES, CLIMATE-CHANGE, ANFIS, AVIATION, NETWORK, AIRPORT, PARTICLE
  • Anadolu University Affiliated: No

Abstract

This paper presents an exhaust gas emissions prediction model for a turboprop engine depending on some performance parameters. Within this context, experimentally collected emissions data is used to develop a model in the adaptive network-based fuzzy inference system. For system identification in the adaptive network-based fuzzy inference system, grid partitioning is preferred as the clustering method, and the accuracy of the prediction model is acceptable to the best of the authors' knowledge. The root mean square error is found to be 0.12375, 4.7332, 0.081264 and 0.033515 for the emissions index prediction of carbon monoxide, carbon dioxide, nitrogen oxides and unburned hydrocarbons, respectively.